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Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image

Xunping Wang¹, Wei Yuan²

¹School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.

|June 4, 2024

Summary

This summary is machine-generated.

A new nuclei-level prior knowledge constrained multiple instance learning (NPKC-MIL) model enhances breast cancer whole slide image classification. This approach integrates deep learning with prior biological knowledge for improved interpretability and accuracy.

Keywords:

Bioinformatics Machine learning

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Area of Science:

Oncology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Breast cancer is now the most common cancer globally, surpassing lung cancer.
Deep learning models in medical image analysis, particularly for whole slide images (WSIs), lack interpretability, hindering pathologist adoption.
Existing methods struggle to integrate prior biological knowledge into deep learning frameworks for WSI classification.

Purpose of the Study:

To propose a novel nuclei-level prior knowledge constrained multiple instance learning (NPKC-MIL) framework for breast whole slide image (WSI) classification.
To enhance the interpretability and performance of deep learning models in digital pathology.
To integrate handcrafted nuclei-level features with deep learning-based patch and slide-level features.

Main Methods:

Utilized transfer learning for patch-level feature extraction and attention pooling for slide-level feature aggregation.
Employed the K-Nearest Neighbors (K-NN) algorithm to establish nucleus topology and generate handcrafted features for nuclei nodes.
Combined deep learning features (patch-level and slide-level) with handcrafted nuclei-level features for model fine-tuning.

Main Results:

The proposed NPKC-MIL model demonstrated superior performance compared to existing deep learning models in WSI classification.
The integration of nuclei-level prior knowledge significantly improved the classification accuracy and interpretability.
NPKC-MIL successfully expanded the analytical dimensions for WSI classification tasks.

Conclusions:

NPKC-MIL offers a promising approach for interpretable and accurate breast cancer WSI classification.
Integrating prior biological knowledge into deep learning models is crucial for advancing digital pathology.
This framework has the potential to improve diagnostic accuracy and clinical adoption of AI in cancer detection.